Putting ubiquitous crowd-sourcing into context

Mashhadi, Afra; Quattrone, Giovanni; Capra, Licia

doi:10.1145/2441776.2441845

Cited by 41 publications

(28 citation statements)

References 20 publications

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“…Quality control mechanism similar to those employed in crowdsourcing (i.e., majority consensus is used to maintain Wikimapia data [70]) as well as use of heuristics (e.g., POI data entry of a café in the middle of a historic park tend to be erroneous [70]) should not be present is also used to assure its data accuracy However, its coverage still falls behind that of other official dataset, especially in rural areas and in countries where OpenStreetMap is less popular [133]. This geographic data coverage bias is common in VGI [74,124,145]. In chapter 6, we investigate the quality and quantity of data collected via our remote data collection methods to see if our volunteer-based methods are viable for collecting geographical information about street-level accessibility.…”

Section: Volunteered Geographic Informationmentioning

confidence: 99%

Scalable methods to collect and visualize sidewalk accessibility data for people with mobility impairments

Hara

2015

SIGACCESS Access. Comput.

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Poorly maintained sidewalks pose considerable accessibility challenges for people with mobility impairments. Despite comprehensive civil rights legislation of Americans with Disabilities Act, many city streets and sidewalks in the U.S. remain inaccessible.The problem is not just that sidewalk accessibility fundamentally affects where and how people travel in cities, but also that there are few, if any, mechanisms to determine accessible areas of a city a priori.To address this problem, my Ph.D. dissertation introduces and evaluates new scalable methods for collecting data about street-level accessibility using a combination of crowdsourcing, automated methods, and Google Street View (GSV). My dissertation has four research threads. First, we conduct a formative interview study to establish a better understanding of how people with mobility impairments currently assess accessibility in the built environment and the role of emerging location-based technologies therein. The study uncovers the existing methods for assessing accessibility of physical environment and identify useful features of future assistive technologies. Second, we develop and evaluate scalable crowdsourced accessibility data collection methods. We show that paid crowd workers recruited from an online labor marketplace can find and label accessibility attributes in GSV with accuracy of 81%. This accuracy improves to 93% with quality control mechanisms such as majority vote. Third, we design a system that combines crowdsourcing and automated methods to increase data collection efficiency. Our work shows that by combining crowdsourcing and automated methods, we can increase data collection efficiency by 13% without sacrificing accuracy. Fourth, we develop and deploy a web tool that lets volunteers to help us collect the street-level accessibility data from Washington, D.C.As of writing this dissertation, we have collected the accessibility data from 20% of the streets in D.C. We conduct a preliminary evaluation on how the said web tool is used.Finally, we implement proof-of-concept accessibility-aware applications with accessibility data collected with the help of volunteers.My dissertation contributes to the accessibility, computer science, and HCI communities by: (i) extending the knowledge of how people with mobility impairments interact with technology to navigate in cities; (ii) introducing the first work that demonstrates that GSV is a viable source for learning about the accessibility of the physical world; (iii) introducing the first method that combines crowdsourcing and automated methods to remotely collect accessibility information; (iv) deploying interactive web tools that allow volunteers to help populate the largest dataset about street-level accessibility of the world; and (v) demonstrating accessibility-aware applications that empower people with mobility impairments.

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Section: Volunteered Geographic Informationmentioning

confidence: 99%

Scalable methods to collect and visualize sidewalk accessibility data for people with mobility impairments

Hara

2015

SIGACCESS Access. Comput.

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“…Zielstra et al [45] found that road coverage in Germany sharply decreases as we move away from city centres and Girres et al [13] discovered a positive correlation between the number of OSM road 'objects' in an area and number of OSM contributors in that area. Shifting focus from the road network to points-of-interest (POIs), Mashhadi et al [30] found that both socio-economic factors (e.g., income deprivation) and physical distance from the city centre are negatively correlated with coverage in London, UK. Another well known example of urban 7 http://www.navteq.com/ crowd-sourcing application is Cyclopath, 8 a geo-wiki that is being successfully used to digitally map route information for cyclists in Minneapolis.…”

Section: Related Workmentioning

confidence: 99%

“…In terms of spatial unit of analysis, previous studies have shown that factors such as population density and deprivation are correlated with coverage of crowd-sourced urban information [30]. We have thus chosen to operate at a level of granularity in London for which such information is available.…”

Section: Metricsmentioning

confidence: 99%

“…6 However, relying entirely on user-generated content for urban mapping raises concerns, not only in terms of accuracy of the collected information (which, for OSM, is presently high), but also in terms of its coverage. Recent studies of OSM have revealed that coverage of information is non-uniformly distributed across a city, with areas that are either further from the centre, or central but income deprived, being particularly neglected [30]. This finding raises concerns in terms of the long-term sustainability of urban crowdsourcing: is coverage going to spontaneously grow across the city?…”

Section: Introductionmentioning

confidence: 99%

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Modelling growth of urban crowd-sourced information

Quattrone

Mashhadi²,

Quercia

et al. 2014

Proceedings of the 7th ACM International Conference on Web Search and Data Mining

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Urban crowd-sourcing has become a popular paradigm to harvest spatial information about our evolving cities directly from citizens. OpenStreetMap is a successful example of such paradigm, with an accuracy of its user-generated content comparable to that of curated databases (e.g., Ordnance Survey). Coverage is however low and most importantly non-uniformly distributed across the city. Being able to model the spontaneous growth of digital information in these domains is required, so to be able to plan interventions aimed at gathering content about areas that would otherwise be neglected. Inspired by models of physical urban growth developed by urban planners, we build a model of digital growth of crowd-sourced spatial information that is both easy to interpret and dynamic, so to be able to determine what factors impact growth and how these change over time. We build and test the model against five years of OpenStreetMap data for the city of London, UK. We then run the model against two other cities, chosen for their different physical and digital growth's characteristics, so to stress-test the model. We conclude with a discussion of the implications of this work on both developers and users of urban crowd-sourcing applications.

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“…However, despite the great popularity that geo-wiki systems received in the last years, they have seen far less research attention, with the exception of recent studies on accuracy and coverage of OpenStreetMap 1 (OSM) where the accuracy is shown to be high [7,14] and coverage to be non-uniformly distributed [28,13]. However, users behaviour in this domain is by far less investigated subject and thus requires more research effort to be well understood.…”

Section: Introductionmentioning

confidence: 99%

Temporal analysis of activity patterns of editors in collaborative mapping project of OpenStreetMap

Yasseri

Quattrone

Mashhadi³

2013

Proceedings of the 9th International Symposium on Open Collaboration

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In the recent years Wikis have become an attractive platform for social studies of the human behaviour. Containing millions records of edits across the globe, collaborative systems such as Wikipedia have allowed researchers to gain a better understanding of editors participation and their activity patterns. However, contributions made to Geo-wikis -wikibased collaborative mapping projects-differ from systems such as Wikipedia in a fundamental way due to spatial dimension of the content that limits the contributors to a set of those who posses local knowledge about a specific area and therefore cross-platform studies and comparisons are required to build a comprehensive image of online open collaboration phenomena. In this work, we study the temporal behavioural pattern of OpenStreetMap editors, a successful example of geo-wiki, for two European capital cities. We categorise different type of temporal patterns and report on the historical trend within a period of 7 years of the project age. We also draw a comparison with the previously observed editing activity patterns of Wikipedia.

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Putting ubiquitous crowd-sourcing into context

Cited by 41 publications

References 20 publications

Scalable methods to collect and visualize sidewalk accessibility data for people with mobility impairments

Scalable methods to collect and visualize sidewalk accessibility data for people with mobility impairments

Modelling growth of urban crowd-sourced information

Temporal analysis of activity patterns of editors in collaborative mapping project of OpenStreetMap

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